The welding process in aluminum is a complex process that commonly presents several issues such as weld bead discontinuity, cracks, and lack of penetration. Thus, an accurate specification of the parameters in order to achieve optimal values for the investigated responses is aimed by the industry. The present paper proposes the application of a multiobjective optimization approach considering multivariate constraints based on the simultaneous confidence intervals and the elliptical region of the correlated data. Structured experiments for the welding process of aluminum alloy (AA) 6063 TA tubes used in corona rings were performed according to a face-centered composite design with 4 factors, wire feed rate (Wf), arc voltage (V), contact tip to the workpiece distance (Ct), and motor frequency (Fr), resulting in 31 experiments. Poisson regression was applied to model the values of yield (Y), dilution (D), reinforcement index (RI), and penetration index (PI), allowing to estimate the optimal individual values with regard to the multivariate constraints. Rotated factor scores were obtained in order to replace the original data, and therefore, the factor multivariate square error was used as objective functions to be minimized through normal boundary intersection method. A satisfactory weld bead with large values of PI, D, and Y and a small value of RI was reached as prespecified by the manager of the process.